A Survey on Load Balancing Algorithms for VM Placement in Cloud Computing

The emergence of cloud computing based on virtualization technologies brings huge opportunities to host virtual resource at low cost without the need of owning any infrastructure. Virtualization technologies enable users to acquire, configure and be charged on pay-per-use basis. However, Cloud data centers mostly comprise heterogeneous commodity servers hosting multiple virtual machines (VMs) with potential various specifications and fluctuating resource usages, which may cause imbalanced resource utilization within servers that may lead to performance degradation and service level agreements (SLAs) violations. To achieve efficient scheduling, these challenges should be addressed and solved by using load balancing strategies, which have been proved to be NP-hard problem. From multiple perspectives, this work identifies the challenges and analyzes existing algorithms for allocating VMs to PMs in infrastructure Clouds, especially focuses on load balancing. A detailed classification targeting load balancing algorithms for VM placement in cloud data centers is investigated and the surveyed algorithms are classified according to the classification. The goal of this paper is to provide a comprehensive and comparative understanding of existing literature and aid researchers by providing an insight for potential future enhancements.Comment: 22 Pages, 4 Figures, 4 Tables, in pres

Cross-Layer Rapid Prototyping and Synthesis of Application-Specific and Reconfigurable Many-accelerator Platforms

Technological advances of recent years laid the foundation consolidation of informatisationof society, impacting on economic, political, cultural and socialdimensions. At the peak of this realization, today, more and more everydaydevices are connected to the web, giving the term ”Internet of Things”. The futureholds the full connection and interaction of IT and communications systemsto the natural world, delimiting the transition to natural cyber systems and offeringmeta-services in the physical world, such as personalized medical care, autonomoustransportation, smart energy cities etc. . Outlining the necessities of this dynamicallyevolving market, computer engineers are required to implement computingplatforms that incorporate both increased systemic complexity and also cover awide range of meta-characteristics, such as the cost and design time, reliabilityand reuse, which are prescribed by a conflicting set of functional, technical andconstruction constraints. This thesis aims to address these design challenges bydeveloping methodologies and hardware/software co-design tools that enable therapid implementation and efficient synthesis of architectural solutions, which specifyoperating meta-features required by the modern market. Specifically, this thesispresents a) methodologies to accelerate the design flow for both reconfigurableand application-specific architectures, b) coarse-grain heterogeneous architecturaltemplates for processing and communication acceleration and c) efficient multiobjectivesynthesis techniques both at high abstraction level of programming andphysical silicon level.Regarding to the acceleration of the design flow, the proposed methodologyemploys virtual platforms in order to hide architectural details and drastically reducesimulation time. An extension of this framework introduces the systemicco-simulation using reconfigurable acceleration platforms as co-emulation intermediateplatforms. Thus, the development cycle of a hardware/software productis accelerated by moving from a vertical serial flow to a circular interactive loop.Moreover the simulation capabilities are enriched with efficient detection and correctiontechniques of design errors, as well as control methods of performancemetrics of the system according to the desired specifications, during all phasesof the system development. In orthogonal correlation with the aforementionedmethodological framework, a new architectural template is proposed, aiming atbridging the gap between design complexity and technological productivity usingspecialized hardware accelerators in heterogeneous systems-on-chip and networkon-chip platforms. It is presented a novel co-design methodology for the hardwareaccelerators and their respective programming software, including the tasks allocationto the available resources of the system/network. The introduced frameworkprovides implementation techniques for the accelerators, using either conventionalprogramming flows with hardware description language or abstract programmingmodel flows, using techniques from high-level synthesis. In any case, it is providedthe option of systemic measures optimization, such as the processing speed,the throughput, the reliability, the power consumption and the design silicon area.Finally, on addressing the increased complexity in design tools of reconfigurablesystems, there are proposed novel multi-objective optimization evolutionary algo-rithms which exploit the modern multicore processors and the coarse-grain natureof multithreaded programming environments (e.g. OpenMP) in order to reduce theplacement time, while by simultaneously grouping the applications based on theirintrinsic characteristics, the effectively explore the design space effectively.The efficiency of the proposed architectural templates, design tools and methodologyflows is evaluated in relation to the existing edge solutions with applicationsfrom typical computing domains, such as digital signal processing, multimedia andarithmetic complexity, as well as from systemic heterogeneous environments, suchas a computer vision system for autonomous robotic space navigation and manyacceleratorsystems for HPC and workstations/datacenters. The results strengthenthe belief of the author, that this thesis provides competitive expertise to addresscomplex modern - and projected future - design challenges.Οι τεχνολογικές εξελίξεις των τελευταίων ετών έθεσαν τα θεμέλια εδραίωσης της πληροφοριοποίησης της κοινωνίας, επιδρώντας σε οικονομικές,πολιτικές, πολιτιστικές και κοινωνικές διαστάσεις. Στο απόγειο αυτής τη ςπραγμάτωσης, σήμερα, ολοένα και περισσότερες καθημερινές συσκευές συνδέονται στο παγκόσμιο ιστό, αποδίδοντας τον όρο «Ίντερνετ των πραγμάτων».Το μέλλον επιφυλάσσει την πλήρη σύνδεση και αλληλεπίδραση των συστημάτων πληροφορικής και επικοινωνιών με τον φυσικό κόσμο, οριοθετώντας τη μετάβαση στα συστήματα φυσικού κυβερνοχώρου και προσφέροντας μεταυπηρεσίες στον φυσικό κόσμο όπως προσωποποιημένη ιατρική περίθαλψη, αυτόνομες μετακινήσεις, έξυπνες ενεργειακά πόλεις κ.α. . Σκιαγραφώντας τις ανάγκες αυτής της δυναμικά εξελισσόμενης αγοράς, οι μηχανικοί υπολογιστών καλούνται να υλοποιήσουν υπολογιστικές πλατφόρμες που αφενός ενσωματώνουν αυξημένη συστημική πολυπλοκότητα και αφετέρου καλύπτουν ένα ευρύ φάσμα μεταχαρακτηριστικών, όπως λ.χ. το κόστος σχεδιασμού, ο χρόνος σχεδιασμού, η αξιοπιστία και η επαναχρησιμοποίηση, τα οποία προδιαγράφονται από ένα αντικρουόμενο σύνολο λειτουργικών, τεχνολογικών και κατασκευαστικών περιορισμών. Η παρούσα διατριβή στοχεύει στην αντιμετώπιση των παραπάνω σχεδιαστικών προκλήσεων, μέσω της ανάπτυξης μεθοδολογιών και εργαλείων συνσχεδίασης υλικού/λογισμικού που επιτρέπουν την ταχεία υλοποίηση καθώς και την αποδοτική σύνθεση αρχιτεκτονικών λύσεων, οι οποίες προδιαγράφουν τα μετα-χαρακτηριστικά λειτουργίας που απαιτεί η σύγχρονη αγορά. Συγκεκριμένα, στα πλαίσια αυτής της διατριβής, παρουσιάζονται α) μεθοδολογίες επιτάχυνσης της ροής σχεδιασμού τόσο για επαναδιαμορφούμενες όσο και για εξειδικευμένες αρχιτεκτονικές, β) ετερογενή αδρομερή αρχιτεκτονικά πρότυπα επιτάχυνσης επεξεργασίας και επικοινωνίας και γ) αποδοτικές τεχνικές πολυκριτηριακής σύνθεσης τόσο σε υψηλό αφαιρετικό επίπεδο προγραμματισμού,όσο και σε φυσικό επίπεδο πυριτίου.Αναφορικά προς την επιτάχυνση της ροής σχεδιασμού, προτείνεται μια μεθοδολογία που χρησιμοποιεί εικονικές πλατφόρμες, οι οποίες αφαιρώντας τις αρχιτεκτονικές λεπτομέρειες καταφέρνουν να μειώσουν σημαντικά το χρόνο εξομοίωσης. Παράλληλα, εισηγείται η συστημική συν-εξομοίωση με τη χρήση επαναδιαμορφούμενων πλατφορμών, ως μέσων επιτάχυνσης. Με αυτόν τον τρόπο, ο κύκλος ανάπτυξης ενός προϊόντος υλικού, μετατεθειμένος από την κάθετη σειριακή ροή σε έναν κυκλικό αλληλεπιδραστικό βρόγχο, καθίσταται ταχύτερος, ενώ οι δυνατότητες προσομοίωσης εμπλουτίζονται με αποδοτικότερες μεθόδους εντοπισμού και διόρθωσης σχεδιαστικών σφαλμάτων, καθώς και μεθόδους ελέγχου των μετρικών απόδοσης του συστήματος σε σχέση με τις επιθυμητές προδιαγραφές, σε όλες τις φάσεις ανάπτυξης του συστήματος. Σε ορθογώνια συνάφεια με το προαναφερθέν μεθοδολογικό πλαίσιο, προτείνονται νέα αρχιτεκτονικά πρότυπα που στοχεύουν στη γεφύρωση του χάσματος μεταξύ της σχεδιαστικής πολυπλοκότητας και της τεχνολογικής παραγωγικότητας, με τη χρήση συστημάτων εξειδικευμένων επιταχυντών υλικού σε ετερογενή συστήματα-σε-ψηφίδα καθώς και δίκτυα-σε-ψηφίδα. Παρουσιάζεται κατάλληλη μεθοδολογία συν-σχεδίασης των επιταχυντών υλικού και του λογισμικού προκειμένου να αποφασισθεί η κατανομή των εργασιών στους διαθέσιμους πόρους του συστήματος/δικτύου. Το μεθοδολογικό πλαίσιο προβλέπει την υλοποίηση των επιταχυντών είτε με συμβατικές μεθόδους προγραμματισμού σε γλώσσα περιγραφής υλικού είτε με αφαιρετικό προγραμματιστικό μοντέλο με τη χρήση τεχνικών υψηλού επιπέδου σύνθεσης. Σε κάθε περίπτωση, δίδεται η δυνατότητα στο σχεδιαστή για βελτιστοποίηση συστημικών μετρικών, όπως η ταχύτητα επεξεργασίας, η ρυθμαπόδοση, η αξιοπιστία, η κατανάλωση ενέργειας και η επιφάνεια πυριτίου του σχεδιασμού. Τέλος, προκειμένου να αντιμετωπισθεί η αυξημένη πολυπλοκότητα στα σχεδιαστικά εργαλεία επαναδιαμορφούμενων συστημάτων, προτείνονται νέοι εξελικτικοί αλγόριθμοι πολυκριτηριακής βελτιστοποίησης, οι οποίοι εκμεταλλευόμενοι τους σύγχρονους πολυπύρηνους επεξεργαστές και την αδρομερή φύση των πολυνηματικών περιβαλλόντων προγραμματισμού (π.χ. OpenMP), μειώνουν το χρόνο επίλυσης του προβλήματος της τοποθέτησης των λογικών πόρων σε φυσικούς,ενώ ταυτόχρονα, ομαδοποιώντας τις εφαρμογές βάση των εγγενών χαρακτηριστικών τους, διερευνούν αποτελεσματικότερα το χώρο σχεδίασης.Η αποδοτικότητά των προτεινόμενων αρχιτεκτονικών προτύπων και μεθοδολογιών επαληθεύτηκε σε σχέση με τις υφιστάμενες λύσεις αιχμής τόσο σε αυτοτελής εφαρμογές, όπως η ψηφιακή επεξεργασία σήματος, τα πολυμέσα και τα προβλήματα αριθμητικής πολυπλοκότητας, καθώς και σε συστημικά ετερογενή περιβάλλοντα, όπως ένα σύστημα όρασης υπολογιστών για αυτόνομα διαστημικά ρομποτικά οχήματα και ένα σύστημα πολλαπλών επιταχυντών υλικού για σταθμούς εργασίας και κέντρα δεδομένων, στοχεύοντας εφαρμογές υψηλής υπολογιστικής απόδοσης (HPC). Τα αποτελέσματα ενισχύουν την πεποίθηση του γράφοντα, ότι η παρούσα διατριβή παρέχει ανταγωνιστική τεχνογνωσία για την αντιμετώπιση των πολύπλοκων σύγχρονων και προβλεπόμενα μελλοντικών σχεδιαστικών προκλήσεων

A Hybrid Optimization Algorithm for Efficient Virtual Machine Migration and Task Scheduling Using a Cloud-Based Adaptive Multi-Agent Deep Deterministic Policy Gradient Technique

This To achieve optimal system performance in the quickly developing field of cloud computing, efficient resource management—which includes accurate job scheduling and optimized Virtual Machine (VM) migration—is essential. The Adaptive Multi-Agent System with Deep Deterministic Policy Gradient (AMS-DDPG) Algorithm is used in this study to propose a cutting-edge hybrid optimization algorithm for effective virtual machine migration and task scheduling. An sophisticated combination of the War Strategy Optimization (WSO) and Rat Swarm Optimizer (RSO) algorithms, the Iterative Concept of War and Rat Swarm (ICWRS) algorithm is the foundation of this technique. Notably, ICWRS optimizes the system with an amazing 93% accuracy, especially for load balancing, job scheduling, and virtual machine migration. The VM migration and task scheduling flexibility and efficiency are greatly improved by the AMS-DDPG technology, which uses a powerful combination of deterministic policy gradient and deep reinforcement learning. By assuring the best possible resource allocation, the Adaptive Multi-Agent System method enhances decision-making even more. Performance in cloud-based virtualized systems is significantly enhanced by our hybrid method, which combines deep learning and multi-agent coordination. Extensive tests that include a detailed comparison with conventional techniques verify the effectiveness of the suggested strategy. As a consequence, our hybrid optimization approach is successful. The findings show significant improvements in system efficiency, shorter job completion times, and optimum resource utilization. Cloud-based systems have unrealized potential for synergistic optimization, as shown by the integration of ICWRS inside the AMS-DDPG framework. Enabling a high-performing and sustainable cloud computing infrastructure that can adapt to the changing needs of modern computing paradigms is made possible by this strategic resource allocation, which is attained via careful computational utilization

Multi-capacity combinatorial ordering GA in application to cloud resources allocation and efficient virtual machines consolidation

This paper describes a novel approach making use of genetic algorithms to find optimal solutions for multi-dimensional vector bin packing problems with the goal to improve cloud resource allocation and Virtual Machines (VMs) consolidation. Two algorithms, namely Combinatorial Ordering First-Fit Genetic Algorithm (COFFGA) and Combinatorial Ordering Next Fit Genetic Algorithm (CONFGA) have been developed for that and combined. The proposed hybrid algorithm targets to minimise the total number of running servers and resources wastage per server. The solutions obtained by the new algorithms are compared with latest solutions from literature. The results show that the proposed algorithm COFFGA outperforms other previous multi-dimension vector bin packing heuristics such as Permutation Pack (PP), First Fit (FF) and First Fit Decreasing (FFD) by 4%, 34%, and 39%, respectively. It also achieved better performance than the existing genetic algorithm for multi-capacity resources virtual machine consolidation (RGGA) in terms of performance and robustness. A thorough explanation for the improved performance of the newly proposed algorithm is given

OPTIMIZING SERVER CONSOLIDATION FOR ENTERPRISE APPLICATION SERVICE PROVIDERS

In enterprise application environments, hardware resources show averagely low utilization rates due to a provisioning practice that is based on peak demands. Therefore, the consolidation of orthogonal workloads can improve energy efficiency and reduce total cost of ownership. In this paper, we address existing workload consolidation potential by solving a bin packing problem, where the number of servers is to be minimized. Since dynamic workloads, gathered from historical traces, and priorities of running services are considered, we formulate the Dynamic Priority-based Workload Consolidation Problem (DPWCP) and develop solution algorithms using heuristics and metaheuristics. Relevance is pointed out by an analysis of service resource demands and server capacities across four studied cases from productively operating enterprise application service providers. After a classification of related work, seven algorithms were developed and evaluated regarding their exploited optimization potential and computing time. Best results were achieved by a best-fit approach that uses a genetic algorithm to optimize its input sequence (GA_BF). When applying the GA_BF onto the four studied cases, average utilization rates could be increased from 23 to 63 percent within an average computing time of 22.5 seconds. Therefore, the overall server capacity was reduced significantly by up to 83%

A novel energy-efficient resource allocation algorithm based on immune clonal optimization for green cloud computing

Cloud computing is a style of computing in which dynamically scalable and other virtualized resources are provided as a service over the Internet. The energy consumption and makespan associated with the resources allocated should be taken into account. This paper proposes an improved clonal selection algorithm based on time cost and energy consumption models in cloud computing environment. We have analyzed the performance of our approach using the CloudSim toolkit. The experimental results show that our approach has immense potential as it offers significant improvement in the aspects of response time and makespan, demonstrates high potential for the improvement in energy efficiency of the data center, and can effectively meet the service level agreement requested by the users.Comment: arXiv admin note: text overlap with arXiv:1006.0308 by other author

Multi-objective ACO resource consolidation in cloud computing environment

Cloud computing systems provide services to users based on a pay-as-you-go model. High volume of interest and a number of requests by user in cloud computing has resulted in the creation of data centers with large amounts of physical machines. These data centers consume huge amounts of electrical energy and air emissions. In order to improve Datacenter efficiency, resource consolidation using virtualization technology is becoming important for the reduction of the environmental impact caused by the data centers (e.g. electricity usage and carbon dioxide). By using Virtualization technology multiple VM (logical slices that conceptually called VMs) instances can be initialised on a physical machine. As a result, the amounts of active hardware are reduced and the utilisations of physical resources are increased. The present thesis focuses on problem of virtual machine placement and virtual machine consolidation in cloud computing environment. VM placement is a process of mapping virtual machines (Beloglazov and Buyya) to physical machines (PMs). VM consolidation reallocates and optimizes the mapping of VMs and PMs based on migration technique. The goal is to minimize energy consumption, resource wastage and energy communication cost between network elements within a data center under QoS constraints through VM placement and VM consolidation algorithms. The multi objective algorithms are proposed to control trade-off between energy, performance and quality of services. The algorithms have been analyzed with other approaches using Cloudsim tools. The results demonstrate that the proposed algorithms can seek and find solutions that exhibit balance between different objectives. Our main contributions are the proposal of a multi- objective optimization placement approach in order to minimize the total energy consumption of a data center, resource wastage and energy communication cost. Another contribution is to propose a multiobjective consolidation approach in order to minimize the total energy consumption of a data center, minimize number of migrations, minimize number of PMs and reconfigure resources to satisfy the SLA. Also the results have been compared with other single-objective and multi-objective algorithms