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Emerging Internet of Things (IoT) applications often demonstrate unpredictable Big Data processing workloads at the cloud backends making it hard for cloud service providers (CSPs) to employ existing resource overbooking schemes effectively. Ad hoc approaches to resource overbooking can lead to performance interference among the virtual machines (VMs) hosted on the physical resources causing performance...
In recent years, there has been a rapid growth of interest in dynamic management of resources in virtualized systems. Virtualization provides great flexibility in terms of resource sharing but at the same time it also brings new challenges for load balancing using automatic migrations of virtual machines. In this paper, we have evaluated VMware's Distributed Resource Scheduler (DRS) in a number of...
Middleboxes are adopted to enhance network functions, e.g., providing security and guaranteeing performance. Some applications require flows to sequence through several middleboxes, and such stateful policy routing is challenging for network administrators. The goal of the management is to distribute well middlebox resources and avoid overloads, thus protecting networks from malfunctioning. Furthermore,...
Accurate and efficient network-wide traffic measurement is crucial for network management. Recently, Software-defined networking (SDN) has opened up new opportunities in network measurement and inference. In this work, we demonstrate an efficient flow measurement and inference framework which performs adaptive measurement with online learning. Using the reprogrammability of SDN, we assist network...
Nowadays, a datacenter infrastructure with static al- location of resources could be a bottleneck towards a high-quality network service due to growing and unpredictable demands for resources by modern applications. Additionally, an efficient utilization of the available resources could represent economical benefits to service providers. In this context, Voice over IP (VoIP) service providers must...
Cloud computing has emerged as a flexible and efficient paradigm to provide IT resources on-demand. However, it has also raised new challenges for infrastructure providers to manage large-scale deployments in an efficient and effective way. In this paper, we present the trade-off between energy consumption and performance. We outline a novel framework for efficient and effective resource consolidation...
Over the last few years, the context of big data has gained a significant traction due to many factors. While the public cloud model had been deeply studied to face the increasing demand for large-scale data processing capabilities, many organizations are now focusing on the hybrid cloud model, where the classic scenario is enriched with a private (company owned) cloud -- e.g., for the management...
The use of resources in wireless sensor network's (WSN) is usually highly related to the execution of task which consume reasonable operating and communication bandwidth. To allocate the workload of each task to proper nodes in an efficient manner a task allocation is needed. But it is a typical problem in the area of high performance computing. But this can be overcome, by applying the power and...
We establish a framework that can be used by Origin Servers (content-generating organizations) for claiming Content Delivery Network (CDN) resources in a fine-grained way. The basis of our work lies in the use of Stocks as well as a Secondary Market for the stock trading, tools and products commonly used in modern capital markets. Network and disk resources are being monitored through well-established...
With the rising growth trend of data size in the Internet era, storage, analysis, and processing of big data arebecomingamong the strongtopics in academia and industry. Typical big data processing platforms adopt the MapReduce programming model to perform application processing. For example, the deployment and calculation method of Hadoop are as follows: Hadoop first collects data and stores them...
In modern cloud data centers, tens of thousands of virtual machines are monitored and managed every day. Cloud service providers task is revealed to be complex and time consuming. In order to make it easier, researchers focused on minimizing the number of virtual machine instances to be monitored in fine grain. This is achieved by clustering virtual machines in classes of similar behavior. Projection...
Cloud Computing is the technology provisioning services from remote Cloud Data Centers (CDC) using the Internet technologies where services range from compute to storage. This technology has evolved from distributed computing and it incorporates other technologies namely virtualization, grid and utility computing. The rate of users getting facilitated by the CDC increases exponentially. In this scenario...
Many-core architectures are becoming a major execution platform in order to face the increasing number of applications to be executed in parallel. Such an approach is very attractive in order to offer users with high performance. However it introduces some key challenges in terms of security as some malicious applications may compromise the whole system. A defense-in-depth approach relying on hardware...
Many-core architectures are becoming a major execution platform in order to face the increasing number of applications to be executed in parallel. Such an approach is very attractive in order to offer users with high performance. However it introduces some key challenges in terms of security as some malicious applications may compromise the whole system. A defense-in-depth approach relying on hardware...
Current processors provide a variety of different processing units to improve performance and power efficiency. For example, ARM's big.LITTLE, AMD's APUs, and Oracle's M7 provide heterogeneous processors, on-die GPUs, and on-die accelerators. However, the performance experienced by programs using these processing units can vary widely due to contention from multiprogramming, thermal constraints and...
One of the key element needed to test most large-scale scheduling algorithms is a testing infrastructure. Large scale is of upmost importance as failures and complex behaviors are common occurrences only at such scale. In order to test the reaction of a system to failures or extreme behaviors, it is necessary to be able to create large scale environments. Such an infrastructure must be reproducible...
The report presents the results concerning to the decision of the inverse ecological problem as the element of the small city atmosphere monitoring system functions. The purpose of the inverse problem statement is to estimate a priori unknown pollutants masses and gas discharge flows from the industrial pollution sources with the intersecting dissipation zones. The estimations should be received on...
Shared last-level cache (LLC) management is a critical design issue for heterogeneous multi-cores. In this paper, we observe two major challenges: the contribution of LLC latency to overall performance varies among applications/cores and also across time; overlooking the off-chip latency factor often leads to adverse effects on overall performance. Hence, we propose a Latency Sensitivity-based Cache...
Cyber-physical systems (CPS) are large scale systems highly integrated with the physical environment. Given the changing nature of physical environments, CPS must be able to adapt on-line to new situations while preserving their correct operation. Correctness by construction relies on using formal tools, which suffer from a considerable computationaloverhead especially if executed on-line. As the...
In the dark silicon era, achieving energy-efficient execution on highly heterogeneous platforms requires a much tighter integration of hardware and software solutions. To tackle this problem, we propose a framework for energy-aware task management in heterogeneous embedded systems with several multi-core clusters. The proposed framework integrates a set of novel application-aware management mechanisms...
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