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  • 1.
    Abbas, Zainab
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Al-Shishtawy, Ahmad
    RISE SICS, Stockholm, Sweden.
    Girdzijauskas, Sarunas
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS. RISE SICS, Stockholm, Sweden..
    Vlassov, Vladimir
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Short-Term Traffic Prediction Using Long Short-Term Memory Neural Networks2018Conference paper (Refereed)
    Abstract [en]

    Short-term traffic prediction allows Intelligent Transport Systems to proactively respond to events before they happen. With the rapid increase in the amount, quality, and detail of traffic data, new techniques are required that can exploit the information in the data in order to provide better results while being able to scale and cope with increasing amounts of data and growing cities. We propose and compare three models for short-term road traffic density prediction based on Long Short-Term Memory (LSTM) neural networks. We have trained the models using real traffic data collected by Motorway Control System in Stockholm that monitors highways and collects flow and speed data per lane every minute from radar sensors. In order to deal with the challenge of scale and to improve prediction accuracy, we propose to partition the road network into road stretches and junctions, and to model each of the partitions with one or more LSTM neural networks. Our evaluation results show that partitioning of roads improves the prediction accuracy by reducing the root mean square error by the factor of 5. We show that we can reduce the complexity of LSTM network by limiting the number of input sensors, on average to 35% of the original number, without compromising the prediction accuracy.

  • 2.
    Abbas, Zainab
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Kalavri, Vasiliki
    Systems Group, ETH, Zurich, Switzerland.
    Carbone, Paris
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Vlassov, Vladimir
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Streaming Graph Partitioning: An Experimental Study2018In: Proceedings of the VLDB Endowment, ISSN 2150-8097, E-ISSN 2150-8097, Vol. 11, no 11, p. 1590-1603Article in journal (Refereed)
    Abstract [en]

    Graph partitioning is an essential yet challenging task for massive graph analysis in distributed computing. Common graph partitioning methods scan the complete graph to obtain structural characteristics offline, before partitioning. However, the emerging need for low-latency, continuous graph analysis led to the development of online partitioning methods. Online methods ingest edges or vertices as a stream, making partitioning decisions on the fly based on partial knowledge of the graph. Prior studies have compared offline graph partitioning techniques across different systems. Yet, little effort has been put into investigating the characteristics of online graph partitioning strategies.

    In this work, we describe and categorize online graph partitioning techniques based on their assumptions, objectives and costs. Furthermore, we employ an experimental comparison across different applications and datasets, using a unified distributed runtime based on Apache Flink. Our experimental results showcase that model-dependent online partitioning techniques such as low-cut algorithms offer better performance for communication-intensive applications such as bulk synchronous iterative algorithms, albeit higher partitioning costs. Otherwise, model-agnostic techniques trade off data locality for lower partitioning costs and balanced workloads which is beneficial when executing data-parallel single-pass graph algorithms.

  • 3.
    Abbas, Zainab
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Sigurdsson, Thorsteinn Thorri
    KTH.
    Al-Shishtawy, Ahmad
    RISE Res Inst Sweden, Stockholm, Sweden..
    Vlassov, Vladimir
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Evaluation of the Use of Streaming Graph Processing Algorithms for Road Congestion Detection2018In: 2018 IEEE INT CONF ON PARALLEL & DISTRIBUTED PROCESSING WITH APPLICATIONS, UBIQUITOUS COMPUTING & COMMUNICATIONS, BIG DATA & CLOUD COMPUTING, SOCIAL COMPUTING & NETWORKING, SUSTAINABLE COMPUTING & COMMUNICATIONS / [ed] Chen, JJ Yang, LT, IEEE COMPUTER SOC , 2018, p. 1017-1025Conference paper (Refereed)
    Abstract [en]

    Real-time road congestion detection allows improving traffic safety and route planning. In this work, we propose to use streaming graph processing algorithms for road congestion detection and evaluate their accuracy and performance. We represent road infrastructure sensors in the form of a directed weighted graph and adapt the Connected Components algorithm and some existing graph processing algorithms, originally used for community detection in social network graphs, for the task of road congestion detection. In our approach, we detect Connected Components or communities of sensors with similarly weighted edges that reflect different states in the traffic, e.g., free flow or congested state, in regions covered by detected sensor groups. We have adapted and implemented the Connected Components and community detection algorithms for detecting groups in the weighted sensor graphs in batch and streaming manner. We evaluate our approach by building and processing the road infrastructure sensor graph for Stockholm's highways using real-world data from the Motorway Control System operated by the Swedish traffic authority. Our results indicate that the Connected Components and DenGraph community detection algorithms can detect congestion with accuracy up to approximate to 94% for Connected Components and up to approximate to 88% for DenGraph. The Louvain Modularity algorithm for community detection fails to detect congestion regions for sparsely connected graphs, representing roads that we have considered in this study. The Hierarchical Clustering algorithm using speed and density readings is able to detect congestion without details, such as shockwaves.

  • 4. Kalavri, Vasiliki
    et al.
    Vlassov, Vladimir
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Haridi, Seif
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    High-Level Programming Abstractions for Distributed Graph Processing2018In: IEEE Transactions on Knowledge and Data Engineering, ISSN 1041-4347, E-ISSN 1558-2191, Vol. 30, no 2, p. 305-324Article in journal (Refereed)
    Abstract [en]

    Efficient processing of large-scale graphs in distributed environments has been an increasingly popular topic of research in recent years. Inter-connected data that can be modeled as graphs appear in application domains such as machine learning, recommendation, web search, and social network analysis. Writing distributed graph applications is inherently hard and requires programming models that can cover a diverse set of problems, including iterative refinement algorithms, graph transformations, graph aggregations, pattern matching, ego-network analysis, and graph traversals. Several high-level programming abstractions have been proposed and adopted by distributed graph processing systems and big data platforms. Even though significant work has been done to experimentally compare distributed graph processing frameworks, no qualitative study and comparison of graph programming abstractions has been conducted yet. In this survey, we review and analyze the most prevalent high-level programming models for distributed graph processing, in terms of their semantics and applicability. We review 34 distributed graph processing systems with respect to the graph processing models they implement and we survey applications that appear in recent distributed graph systems papers. Finally, we discuss trends and open research questions in the area of distributed graph processing.

  • 5.
    Khan, Amin M.
    et al.
    Department of Computer Science, UiT The Arctic University of Norway. Tromsø, Norway.
    Freitag, Felix
    Department of Computer Architecture. Universitat Politecnica de Catalunya. Barcelona, Spain .
    Vlassov, Vladimir
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Ha, Phuong Hoai
    Department of Computer Science, UiT The Arctic University of Norway. Tromsø, Norway.
    Demo Abstract: Towards IoT Service Deployments on Edge Community Network Microclouds2018Conference paper (Refereed)
    Abstract [en]

    Internet of Things (IoT) services for personal devices and smart homes provided by commercial solutions are typically proprietary and closed. These services provide little control to the end users, for instance to take ownership of their data and enabling services, which hinders these solutions' wider acceptance. In this demo paper, we argue for an approach to deploy professional IoT services on user-controlled infrastructure at the network edge. The users would benefit from the ability to choose the most suitable service from different IoT service offerings, like the one which satisfies their privacy requirements, and third-party service providers could offer more tailored IoT services at customer premises. We conduct the demonstration on microclouds, which have been built with the Cloudy platform in the Guifi.net community network. The demonstration is conducted from the perspective of end users, who wish to deploy professional IoT data management and analytics services in volunteer microclouds.

  • 6.
    Khan, Amin M.
    et al.
    UiT Arctic Univ Norway, Dept Comp Sci, Tromso, Norway.;Hitachi Vantara Corp, Lisbon, Portugal..
    Freitag, Felix
    Univ Politecn Cataluna, Dept Comp Architecture, Barcelona, Spain..
    Vlassov, Vladimir
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Ha, Phuong Hoai
    Demo Abstract: Towards IoT Service Deployments on Edge Community Network Microclouds2018In: IEEE INFOCOM 2018 - IEEE CONFERENCE ON COMPUTER COMMUNICATIONS WORKSHOPS (INFOCOM WKSHPS), IEEE , 2018Conference paper (Refereed)
    Abstract [en]

    Internet of Things (IoT) services for personal devices and smart homes provided by commercial solutions are typically proprietary and closed. These services provide little control to the end users, for instance to take ownership of their data and enabling services, which hinders these solutions' wider acceptance. In this demo paper, we argue for an approach to deploy professional IoT services on user-controlled infrastructure at the network edge. The users would benefit from the ability to choose the most suitable service from different IoT service offerings, like the one which satisfies their privacy requirements, and third-party service providers could offer more tailored IoT services at customer premises. We conduct the demonstration on microclouds, which have been built with the Cloudy platform in the Guifi. net community network. The demonstration is conducted from the perspective of end users, who wish to deploy professional IoT data management and analytics services in volunteer microclouds.

  • 7. Koubarakis, M.
    et al.
    Bereta, K.
    Bilidas, D.
    Giannousis, K.
    Ioannidis, T.
    Pantazi, D. -A
    Stamoulis, G.
    Haridi, Seif
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Vlassov, Vladimir
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Bruzzone, L.
    Paris, C.
    Eltoft, T.
    Krämer, T.
    Charalabidis, A.
    Karkaletsis, V.
    Konstantopoulos, S.
    Dowling, J.
    Kakantousis, T.
    Datcu, M.
    Dumitru, C. O.
    Appel, F.
    Bach, H.
    Migdall, S.
    Hughes, N.
    Arthurs, D.
    Fleming, A.
    From copernicus big data to extreme earth analytics2019In: Advances in Database Technology - EDBT, OpenProceedings, 2019, p. 690-693Conference paper (Refereed)
    Abstract [en]

    Copernicus is the European programme for monitoring the Earth. It consists of a set of systems that collect data from satellites and in-situ sensors, process this data and provide users with reliable and up-to-date information on a range of environmental and security issues. The data and information processed and disseminated puts Copernicus at the forefront of the big data paradigm, giving rise to all relevant challenges, the so-called 5 Vs: volume, velocity, variety, veracity and value. In this short paper, we discuss the challenges of extracting information and knowledge from huge archives of Copernicus data. We propose to achieve this by scale-out distributed deep learning techniques that run on very big clusters offering virtual machines and GPUs. We also discuss the challenges of achieving scalability in the management of the extreme volumes of information and knowledge extracted from Copernicus data. The envisioned scientific and technical work will be carried out in the context of the H2020 project ExtremeEarth which starts in January 2019.

  • 8. Lin, X.
    et al.
    Buyya, R.
    Yang, L.
    Tari, Z.
    Choo, K. -KR.
    Vlassov, Vladimir
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Yao, L.
    Yin, H.
    Wang, W.
    Message from the BDCloud 2018 Chairs2019In: 16th IEEE International Symposium on Parallel and Distributed Processing with Applications, 17th IEEE International Conference on Ubiquitous Computing and Communications, 8th IEEE International Conference on Big Data and Cloud Computing, 11th IEEE International Conference on Social Computing and Networking and 8th IEEE International Conference on Sustainable Computing and Communications, ISPA/IUCC/BDCloud/SocialCom/SustainCom 2018, p. XXIX-XXX, article id 8672358Article in journal (Refereed)
  • 9.
    Liu, Ying
    et al.
    KTH.
    Gureya, Daharewa
    KTH.
    Al-Shishtawy, Ahmad
    Vlassov, Vladimir
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    OnlineElastMan: self-trained proactive elasticity manager for cloud-based storage services2017In: Cluster Computing, ISSN 1386-7857, E-ISSN 1573-7543, Vol. 20, no 3, p. 1977-1994Article in journal (Refereed)
    Abstract [en]

    The pay-as-you-go pricing model and the illusion of unlimited resources in the Cloud initiate the idea to provision services elastically. Elastic provisioning of services allocates/de-allocates resources dynamically in response to the changes of the workload. It minimizes the service provisioning cost while maintaining the desired service level objectives (SLOs). Model-predictive control is often used in building such elasticity controllers that dynamically provision resources. However, they need to be trained, either online or offline, before making accurate scaling decisions. The training process involves tedious and significant amount of work as well as some expertise, especially when the model has many dimensions and the training granularity is fine, which is proved to be essential in order to build an accurate elasticity controller. In this paper, we present OnlineElastMan, which is a self-trained proactive elasticity manager for cloud-based storage services. It automatically evolves itself while serving the workload. Experiments using OnlineElastMan with Cassandra indicate that OnlineElastMan continuously improves its provision accuracy, i.e., minimizing provisioning cost and SLO violations, under various workload patterns.

  • 10. Liu, Ying
    et al.
    Gureya, Daharewa
    KTH, School of Information and Communication Technology (ICT).
    Al-Shishtawy, Ahmad
    Vlassov, Vladimir
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    OnlineElastMan: Self-Trained Proactive Elasticity Manager for Cloud-Based Storage Services2016In: 2016 INTERNATIONAL CONFERENCE ON CLOUD AND AUTONOMIC COMPUTING (ICCAC), Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 50-59Conference paper (Refereed)
    Abstract [en]

    The pay-as-you-go pricing model and the illusion of unlimited resources in the Cloud initiate the idea to provision services elastically. Elastic provisioning of services allocates/deallocates resources dynamically in response to the changes of the workload. It minimizes the service provisioning cost while maintaining the desired service level objectives (SLOs). Model-predictive control is often used in building such elasticity controllers that dynamically provision resources. However, they need to be trained, either online or offline, before making accurate scaling decisions. The training process involves tedious and significant amount of work as well as some expertise, especially when the model has many dimensions and the training granularity is fine, which is proved to be essential in order to build an accurate elasticity controller. In this paper, we present OnlineElastMan, which is a self-trained proactive elasticity manager for cloud-based storage services. It automatically trains and evolves itself while serving the workload. Experiments using OnlineElastMan with Cassandra indicate that OnlineElastMan continuously improves its provision accuracy, i.e., minimizing provisioning cost and SLO violations, under various workload patterns.

  • 11.
    Liu, Ying
    et al.
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Li, Xiaxi
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Vlassov, Vladimir
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    GlobLease: A Globally Consistent and Elastic Storage System using Leases2014In: 2014 20TH IEEE INTERNATIONAL CONFERENCE ON PARALLEL AND DISTRIBUTED SYSTEMS (ICPADS), IEEE , 2014, p. 701-709Conference paper (Refereed)
    Abstract [en]

    Nowadays, more and more IT companies are expanding their businesses and services to a global scale, serving users in several countries. Globally distributed storage systems are employed to reduce data access latency for clients all over the world. We present GlobLease, an elastic, globally-distributed and consistent key-value store. It is organised as multiple distributed hash tables (DHTs) storing replicated data and namespace. Across DHTs, data lookups and accesses are processed with respect to the locality of DHT deployments. We explore the use of leases in GlobLease to maintain data consistency across DHTs. The leases enable GlobLease to provide fast and consistent read access in a global scale with reduced global communications. The write accesses are optimized by migrating the master copy to the locations, where most of the writes take place. The elasticity of GlobLease is provided in a fine-grained manner in order to precisely and efficiently handle spiky and skewed read workloads. In our evaluation, GlobLease has demonstrated its optimized global performance, in comparison with Cassandra, with read and write latency less than 10 ms in most of the cases. Furthermore, our evaluation shows that GlobLease is able to bring down the request latency under an instant 4.5 times workload increase with skewed key distribution (a zipfian distribution with an exponent factor of 4) in less than 20 seconds.

  • 12.
    Rameshan, Navaneeth
    et al.
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Liu, Ying
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Navarro, Leandro
    Vlassov, Vladimir
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Augmenting Elasticity Controllers for Improved Accuracy2016In: 2016 IEEE INTERNATIONAL CONFERENCE ON AUTONOMIC COMPUTING (ICAC), IEEE Computer Society, 2016, p. 117-126Conference paper (Refereed)
    Abstract [en]

    Elastic resource provisioning is used to guarantee service level objectives (SLO) at reduced cost in a Cloud platform. However, performance interference in the hosting platform introduces uncertainty in the performance guarantees of provisioned services. Existing elasticity controllers are either unaware of this interference or over-provision resources to meet the SLO. In this paper, we show that assuming predictable performance of VMs in a multi-tenant environment to scale, will result in long periods of SLO violations. We augment the elasticity controller to be aware of interference and improve the convergence time of scaling without over provisioning. We perform experiments with Memcached and compare our solution against a baseline elasticity controller that is unaware of performance interference. Our results show that augmentation can reduce SLO violations by 65% or more and also save provisioning costs compared to an interference oblivious controller.

  • 13. Rameshan, Navaneeth
    et al.
    Liu, Ying
    KTH.
    Navarro, Leandro
    Vlassov, Vladimir
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Elastic Scaling in the Cloud: A Multi-Tenant Perspective2016In: 2016 IEEE 36TH INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING SYSTEMS WORKSHOPS (ICDCSW 2016), IEEE conference proceedings, 2016, p. 25-30Conference paper (Refereed)
    Abstract [en]

    Performance interference in the hosting platform introduces uncertainty in the performance guarantees of provisioned services. Existing elasticity controllers are either unaware of this interference or over-provision resources to meet the SLO. In this paper, we take a holistic view on elastic scaling from a multi-tenant perspective. We show that performance interference can significantly impact the accuracy of scaling and result in long periods of SLO violation. Using Memcached as a case-study, we show that making an elasticity controller interference aware can improve the accuracy of scaling decisions and significantly reduce the periods of SLO violation.

  • 14.
    Sozinov, Konstantin
    et al.
    KTH.
    Vlassov, Vladimir
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Girdzijauskas, Sarunas
    KTH, School of Electrical Engineering and Computer Science (EECS), Software and Computer systems, SCS.
    Human Activity Recognition Using Federated Learning2018In: 2018 IEEE INT CONF ON PARALLEL & DISTRIBUTED PROCESSING WITH APPLICATIONS, UBIQUITOUS COMPUTING & COMMUNICATIONS, BIG DATA & CLOUD COMPUTING, SOCIAL COMPUTING & NETWORKING, SUSTAINABLE COMPUTING & COMMUNICATIONS / [ed] Chen, JJ Yang, LT, IEEE COMPUTER SOC , 2018, p. 1103-1111Conference paper (Refereed)
    Abstract [en]

    State-of-the-art deep learning models for human activity recognition use large amount of sensor data to achieve high accuracy. However, training of such models in a data center using data collected from smart devices leads to high communication costs and possible privacy infringement. In order to mitigate aforementioned issues, federated learning can be employed to train a generic classifier by combining multiple local models trained on data originating from multiple clients. In this work we evaluate federated learning to train a human activity recognition classifier and compare its performance to centralized learning by building two models, namely a deep neural network and a softmax regression trained on both synthetic and real-world datasets. We study communication costs as well as the influence of erroneous clients with corrupted data in federated learning setting. We have found that federated learning for the task of human activity recognition is capable of producing models with slightly worse, but acceptable, accuracy compared to centralized models. In our experiments federated learning achieved an accuracy of up to 89 % compared to 93 % in centralized training for the deep neural network. The global model trained with federated learning on skewed datasets achieves accuracy comparable to centralized learning. Furthermore, we identified an important issue of clients with corrupted data and proposed a federated learning algorithm that identifies and rejects erroneous clients. Lastly, we have identified a trade-off between communication cost and the complexity of a model. We show that more complex models such as deep neural network require more communication in federated learning settings for human activity recognition compared to less complex models, such as multinomial logistic regression.

  • 15.
    Vlassov, Vladimir
    et al.
    KTH.
    Bohn, R.
    Message from the ICCAC 2017 Program Chairs2017In: 4th IEEE International Conference on Cloud and Autonomic Computing, ICCAC 2017, article id 8064047Article in journal (Refereed)
  • 16. Xhagjika, V.
    et al.
    Escoda, O. D.
    Navarro, L.
    Vlassov, Vladimir
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Load and video performance patterns of a cloud based WebRTC Architecture2017In: Proceedings - 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, CCGRID 2017, Institute of Electrical and Electronics Engineers Inc. , 2017, p. 739-744Conference paper (Refereed)
    Abstract [en]

    Web Real-Time Communication or Realtime communication in the Web (WebRTC/RTCWeb) is a prolific new standard and technology stack, providing full audio/video agnostic communications for the Web. Service providers implementing such technology deal with various levels of complexity ranging anywhere from: high service distribution, multi-client integration, P2P and Cloud assisted communication backends, content delivery, real-Time constraints and across clouds resource allocation. This work presents a study of the joint factors including multi-cloud distribution, network performance, media parameters and back-end resource loads, in Cloud based Media Selective Forwarding Units for WebRTC infrastructures. The monitored workload is sampled from a large population of real users of our testing infrastructure, additionally the performance data is sampled both by passive user measurements as well as server side measurements. Patterns correlating such factors enable designing adaptive resource allocation algorithms and defining media Service Level Objectives (SLO) spanning over multiple data-centers or servers. Based on our analysis, we discover strong periodical load patterns even though the nature of user interaction with the system is mostly not predetermined with variable user churn.

  • 17.
    Xhagjika, Vamis
    et al.
    KTH.
    Divorra Escoda, Oscar
    Navarro, Leandro
    Vlassov, Vladimir
    KTH, School of Electrical Engineering (EES).
    Media Streams Allocation and Load Patterns for a WebRTC Cloud Architecture2017In: PROCEEDINGS OF THE 2017 8TH INTERNATIONAL CONFERENCE ON THE NETWORK OF THE FUTURE (NOF) / [ed] Mahmoodi, T Secci, S Cianfrani, A Idzikowski, F, IEEE , 2017, p. 14-21Conference paper (Refereed)
    Abstract [en]

    Web Real-Time Communication Web Real-Time Communication (WebRTC) is seeing a rapid rise in adoption footprint. This standard provides an audio/video platform-agnostic communications framework for the Web build-in right in the browser. The complex technology stack of a full implementation of the standard is vast and includes elements of various computational disciplines like: content delivery, audio/video processing, media transport and quality of experience control, for both P2P and Cloud relayed communications. To the best of our knowledge, no previous study examines the impact of Cloud back-end load and media quality at production scale for a media stream processing application, as well as load mitigation for Cloud media Selective Forwarding Units. The contribution of this work is the analysis and exploitation of server workload (predictable session size, strong periodical load patterns) and media bit rate patterns that are derived from real user traffic (toward our test environment), over an extended period of time. Additionally, a simple and effective load balancing scheme is discussed to fairly distribute big sessions over multiple servers by exploiting the discovered patterns of stable session sizes and server load predictability. A Cloud simulation environment was built to compare the performance of the algorithm with other load allocation policies. This work is a basis for more advanced resource allocation algorithms and media Service Level Objectives (SLO) spanning multiple Cloud entities.

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