Resource Allocation in a Network-Based Cloud Computing Environment: Design Challenges

Cloud computing is an increasingly popular computing paradigm, now proving a necessity for utility computing services. Each provider offers a unique service portfolio with a range of resource configurations. Resource provisioning for cloud services in a comprehensive way is crucial to any resource allocation model. Any model should consider both computational resources and network resources to accurately represent and serve practical needs. Another aspect that should be considered while provisioning resources is energy consumption. This aspect is getting more attention from industry and governments parties. Calls of support for the green clouds are gaining momentum. With that in mind, resource allocation algorithms aim to accomplish the task of scheduling virtual machines on data center servers and then scheduling connection requests on the network paths available while complying with the problem constraints. Several external and internal factors that affect the performance of resource allocation models are introduced in this paper. These factors are discussed in detail and research gaps are pointed out. Design challenges are discussed with the aim of providing a reference to be used when designing a comprehensive energy aware resource allocation model for cloud computing data centers.Comment: To appear in IEEE Communications Magazine, November 201

A Survey on Large Scale Metadata Server for Big Data Storage

Big Data is defined as high volume of variety of data with an exponential data growth rate. Data are amalgamated to generate revenue, which results a large data silo. Data are the oils of modern IT industries. Therefore, the data are growing at an exponential pace. The access mechanism of these data silos are defined by metadata. The metadata are decoupled from data server for various beneficial reasons. For instance, ease of maintenance. The metadata are stored in metadata server (MDS). Therefore, the study on the MDS is mandatory in designing of a large scale storage system. The MDS requires many parameters to augment with its architecture. The architecture of MDS depends on the demand of the storage system's requirements. Thus, MDS is categorized in various ways depending on the underlying architecture and design methodology. The article surveys on the various kinds of MDS architecture, designs, and methodologies. This article emphasizes on clustered MDS (cMDS) and the reports are prepared based on a) Bloom filter$-$based MDS, b) Client$-$funded MDS, c) Geo$-$aware MDS, d) Cache$-$aware MDS, e) Load$-$aware MDS, f) Hash$-$based MDS, and g) Tree$-$based MDS. Additionally, the article presents the issues and challenges of MDS for mammoth sized data.Comment: Submitted to ACM for possible publicatio

The Power of d Choices in Scheduling for Data Centers with Heterogeneous Servers

MapReduce framework is the de facto in big data and its applications where a big data-set is split into small data chunks that are replicated on different servers among thousands of servers. The heterogeneous server structure of the system makes the scheduling much harder than scheduling for systems with homogeneous servers. Throughput optimality of the system on one hand and delay optimality on the other hand creates a dilemma for assigning tasks to servers. The JSQ-MaxWeight and Balanced-Pandas algorithms are the states of the arts algorithms with theoretical guarantees on throughput and delay optimality for systems with two and three levels of data locality. However, the scheduling complexity of these two algorithms are way too much. Hence, we use the power of $d$ choices algorithm combined with the Balanced-Pandas algorithm and the JSQ-MaxWeight algorithm, and compare the complexity of the simple algorithms and the power of $d$ choices versions of them. We will further show that the Balanced-Pandas algorithm combined with the power of the $d$ choices, Balanced-Pandas-Pod, not only performs better than simple Balanced-Pandas, but also is less sensitive to the parameter $d$ than the combination of the JSQ-MaxWeight algorithm and the power of the $d$ choices, JSQ-MaxWeight-Pod. In fact in our extensive simulation results, the Balanced-Pandas-Pod algorithm is performing better than the simple Balanced-Pandas algorithm in low and medium loads, where data centers are usually performing at, and performs almost the same as the Balanced-Pandas algorithm at high loads. Note that the load balancing complexity of Balanced-Pandas and JSQ-MaxWeight algorithms are $O(M)$, where $M$ is the number of servers in the system which is in the order of thousands servers, whereas the complexity of Balanced-Pandas-Pod and JSQ-MaxWeight-Pod are $O(1)$, that makes the central scheduler faster and saves energy

Distributed Hierarchical Control versus an Economic Model for Cloud Resource Management

We investigate a hierarchically organized cloud infrastructure and compare distributed hierarchical control based on resource monitoring with market mechanisms for resource management. The latter do not require a model of the system, incur a low overhead, are robust, and satisfy several other desiderates of autonomic computing. We introduce several performance measures and report on simulation studies which show that a straightforward bidding scheme supports an effective admission control mechanism, while reducing the communication complexity by several orders of magnitude and also increasing the acceptance rate compared to hierarchical control and monitoring mechanisms. Resource management based on market-based mechanisms can be seen as an intermediate step towards cloud self-organization, an ideal alternative to current mechanisms for cloud resource management.Comment: 13 pages, 4 figure

Decentralized Edge-to-Cloud Load-balancing: Service Placement for the Internet of Things

Internet of Things (IoT) requires a new processing paradigm that inherits the scalability of the cloud while minimizing network latency using resources closer to the network edge. Building up such flexibility within the edge-to-cloud continuum consisting of a distributed networked ecosystem of heterogeneous computing resources is challenging. Load-balancing for fog computing becomes a cornerstone for cost-effective system management and operations. This paper studies two optimization objectives and formulates a decentralized load-balancing problem for IoT service placement: (global) IoT workload balance and (local) quality of service, in terms of minimizing the cost of deadline violation, service deployment, and unhosted services. The proposed solution, EPOS Fog, introduces a decentralized multiagent system for collective learning that utilizes edge-to-cloud nodes to jointly balance the input workload across the network and minimize the costs involved in service execution. The agents locally generate possible assignments of requests to resources and then cooperatively select an assignment such that their combination maximizes edge utilization while minimizes service execution cost. Extensive experimental evaluation with realistic Google cluster workloads on various networks demonstrates the superior performance of EPOS Fog in terms of workload balance and quality of service, compared to approaches such as First Fit and exclusively Cloud-based. The findings demonstrate how distributed computational resources on the edge can be utilized more cost-effectively by harvesting collective intelligence.Comment: 16 pages and 15 figure

GB-PANDAS: Throughput and heavy-traffic optimality analysis for affinity scheduling

Dynamic affinity scheduling has been an open problem for nearly three decades. The problem is to dynamically schedule multi-type tasks to multi-skilled servers such that the resulting queueing system is both stable in the capacity region (throughput optimality) and the mean delay of tasks is minimized at high loads near the boundary of the capacity region (heavy-traffic optimality). As for applications, data-intensive analytics like MapReduce, Hadoop, and Dryad fit into this setting, where the set of servers is heterogeneous for different task types, so the pair of task type and server determines the processing rate of the task. The load balancing algorithm used in such frameworks is an example of affinity scheduling which is desired to be both robust and delay optimal at high loads when hot-spots occur. Fluid model planning, the MaxWeight algorithm, and the generalized $c\mu$-rule are among the first algorithms proposed for affinity scheduling that have theoretical guarantees on being optimal in different senses, which will be discussed in the related work section. All these algorithms are not practical for use in data center applications because of their non-realistic assumptions. The join-the-shortest-queue-MaxWeight (JSQ-MaxWeight), JSQ-Priority, and weighted-workload algorithms are examples of load balancing policies for systems with two and three levels of data locality with a rack structure. In this work, we propose the Generalized-Balanced-Pandas algorithm (GB-PANDAS) for a system with multiple levels of data locality and prove its throughput optimality. We prove this result under an arbitrary distribution for service times, whereas most previous theoretical work assumes geometric distribution for service times. The extensive simulation results show that the GB-PANDAS algorithm alleviates the mean delay and has a better performance than the JSQ-MaxWeight algorithm by twofoldComment: IFIP WG 7.3 Performance 2017 - The 35th International Symposium on Computer Performance, Modeling, Measurements and Evaluation 201

MapReduce Scheduler: A 360-degree view

Undoubtedly, the MapReduce is the most powerful programming paradigm in distributed computing. The enhancement of the MapReduce is essential and it can lead the computing faster. Therefore, here are many scheduling algorithms to discuss based on their characteristics. Moreover, there are many shortcoming to discover in this field. In this article, we present the state-of-the-art scheduling algorithm to enhance the understanding of the algorithms. The algorithms are presented systematically such that there can be many future possibilities in scheduling algorithm through this article. In this paper, we provide in-depth insight on the MapReduce scheduling algorithm. In addition, we discuss various issues of MapReduce scheduler developed for large-scale computing as well as heterogeneous environment.Comment: Journal Articl

Adaptive Event Dispatching in Serverless Computing Infrastructures

Serverless computing is an emerging Cloud service model. It is currently gaining momentum as the next step in the evolution of hosted computing from capacitated machine virtualisation and microservices towards utility computing. The term "serverless" has become a synonym for the entirely resource-transparent deployment model of cloud-based event-driven distributed applications. This work investigates how adaptive event dispatching can improve serverless platform resource efficiency and contributes a novel approach that allows for better scaling and fitting of the platform's resource consumption to actual demand

Computational Cosmology and Astrophysics on Adaptive Meshes using Charm++

Astrophysical and cosmological phenomena involve a large variety of physical processes, and can encompass an enormous range of scales. To effectively investigate these phenomena computationally, applications must similarly support modeling these phenomena on enormous ranges of scales; furthermore, they must do so efficiently on high-performance computing platforms of ever-increasing parallelism and complexity. We describe Enzo-P, a Petascale redesign of the ENZO adaptive mesh refinement astrophysics and cosmology application, along with Cello, a reusable and scalable adaptive mesh refinement software framework, on which Enzo-P is based. Cello's scalability is enabled by the Charm++ Parallel Programming System, whose data-driven asynchronous execution model is ideal for taking advantage of the available but irregular parallelism in adaptive mesh refinement-based applications. We present scaling results on the NSF Blue Waters supercomputer, and outline our future plans to bring Enzo-P to the Exascale Era by targeting highly-heterogeneous accelerator-based platforms.Comment: 5 pages, 6 figures, submitted to SC18 workshop: PAW-AT

A Survey of Methods For Analyzing and Improving GPU Energy Efficiency

Recent years have witnessed a phenomenal growth in the computational capabilities and applications of GPUs. However, this trend has also led to dramatic increase in their power consumption. This paper surveys research works on analyzing and improving energy efficiency of GPUs. It also provides a classification of these techniques on the basis of their main research idea. Further, it attempts to synthesize research works which compare energy efficiency of GPUs with other computing systems, e.g. FPGAs and CPUs. The aim of this survey is to provide researchers with knowledge of state-of-the-art in GPU power management and motivate them to architect highly energy-efficient GPUs of tomorrow.Comment: Accepted with minor revision in ACM Computing Survey Journal (impact factor 3.85, five year impact of 7.85