A review on various optimization techniques of resource provisioning in cloud computing

Cloud computing is the provision of IT resources (IaaS) on-demand using a pay as you go model over the internet.It is a broad and deep platform that helps customers builds sophisticated, scalable applications. To get the full benefits, research on a wide range of topics is needed. While resource over-provisioning can cost users more than necessary, resource under provisioning hurts the application performance. The cost effectiveness of cloud computing highly depends on how well the customer can optimize the cost of renting resources (VMs) from cloud providers. The issue of resource provisioning optimization from cloud-consumer potential is a complicated optimization issue, which includes much uncertainty parameters. There is a much research avenue available for solving this problem as it is in the real-world. Here, in this paper we provide details about various optimization techniques for resource provisioning

Privacy Preserving Auction Based Virtual Machine Instances Allocation Scheme for Cloud Computing Environment

Cloud Computing Environment provides computing resources in the form of Virtual Machines (VMs), to the cloud users through Internet. Auction-based VM instances allocation allows different cloud users to participate in an auction for a bundle of Virtual Machine instances where the user with the highest bid value will be selected as the winner by the auctioneer (Cloud Service Provider) to gain more. In this auction mechanism, individual bid values are revealed to the auctioneer in order to select the winner as a result of which privacy of bid values are lost. In this paper, we proposed an auction scheme to select the winner without revealing the individual bid values to the auctioneer to maintain privacy of bid values. The winner will get the access to the bundle of VM instances. This  scheme relies on a set of cryptographic protocols including Oblivious Transfer (OT) protocol and Yao’s protocol to maintain privacy of bid values

Sistem Real Time Monitoring Pendeteksi Kebakaran Hutan dan Lahan di Provinsi Riau

Kebakaran hutan dan lahan di Riau merupakan bencana alam yang berulang setiap musim kemarau. Kondisi tanah di wilayah Riau merupakan jenis tanah gambut dengan luas total kurang lebih 4,04 juta hektar atau 56,1% dari luas lahan gambut di Sumatera, sehingga sangat rentan terhadap kebakaran. Salah satu solusi dalam penelitian ini adalah merancang sistem kebakaran dengan penerapan teknologi Internet of Things (IoT) untuk mengetahui lebih cepat tanda-tanda kebakaran hutan dan lahan. Dalam mendistribusikan data ke server, pengusul memanfaatkan cloud computing sebagai penyimpanan dan pendistribusian data. Partikel Argon (Photon) berguna untuk koneksi wifi yang kuat, selain itu juga diperlukan sensor IR Fire Detector sebagai perangkat yang mendeteksi kebakaran berteknologi tinggi dalam menentukan pola spektral yang dipancarkan oleh api. Di sisi lain, penerapan Sensor DHT22 digunakan sebagai pemantauan kualitas udara dan kelembaban dalam mengukur kualitas udara yang akan berguna pada saat kondisi udara yang disebabkan oleh kebakaran. Sedangkan untuk menentukan lokasi kebakaran hutan atau lahan, peneliti menggunakan modul GPS Neo 6m. Dalam mengukur kondisi kadar di udara diperlukan sensor MQ2, dan untuk mendeteksi kadar kelembapan air dalam tanah menggunakan Soil Moisture Sensor. Data yang dihasilkan dari detektor kebakaran yang terletak di titik kawasan hutan dan lahan akan disimpan dalam database lokal. Berdasarkan hasil penelitian yang telah dilakukan maka dapat disimpulkan bahwa alat pendeteksi kebakaran dengan menggunakan parameter api, suhu, asap, kelembaban udara, dan kelembaban tanah dapat bekerja dengan baik. Partikel Argon dapat menerima input dan membuat koneksi sehingga terhubung menggunakan konsep IoT ke web server sehingga pengguna dapat memantau kondisi lahan secara real time

ERAM2 - ENERGY BASED RESOURCE ALLOCATION WITH MINIMUM RECKON AND MAXIMUM RECKON

The emerging field of cloud computing has flexibility and dominant computational architecture that offers ubiquitous services to users. It is different from traditional architecture because it accommodates resources in a unified way. Due to rapid growth in demands for providing the resources and computation in cloud environments, Resource allocation is considered as primary issues in performance, efficiency, and cost.  For the provisioning of resource, Virtual Machine (VMs) is employed to reduce the response time and executing the tasks according to the available resources.  The users utilize the VMs based on the characteristics of the tasks for effective usage of resources. This helps in load balancing and avoids VMs being in an idle state. Several resource allocation techniques are proposed to maximize the utility of physical resource and minimize the consuming cost of Virtual Machines (VMs). This paper proposes an Energy-Based Resource Allocation with Minimum Reckon and Maximum Reckon (ERAM2); which achieves an efficient scheduling by matching the user tasks on Resource parameters like Accessibility, Availability, Cost, Reliability, Reputation, Response time, Scalability and Throughput in the terms of Maximum Reckon and Minimum Reckon. This paper proposes an Ant Colony - Maximum Reckon and Minimum Reckon (AC-MRMR) method to consolidate all the available resource based on the pheromone value; the score is calculated for each pheromone value. When the score value exceeds Threshold limit then task migration process is carried out for optimized resource allocation of tasks

A Review on Resource Provisioning Algorithms Optimization Techniques in Cloud Computing

Cloud computing is the provision of IT resources (IaaS) on-demand using a pay as you go model over the internet. It is a broad and deep platform that helps customers builds sophisticated, scalable applications. To get the full benefits, research on a wide range of topics is needed. While resource over provisioning can cost users more than necessary, resource under provisioning hurts the application performance. The cost effectiveness of cloud computing highly depends on how well the customer can optimize the cost of renting resources (VMs) from cloud providers. The issue of resource provisioning optimization from cloud-consumer potential is a complicated optimization issue, which includes much uncertainty parameters. There is a much research avenue available for solving this problem as it is in the real-world. Here, in this paper we provide details about various optimization techniques for resource provisioning

Optimization Approach for Resource Allocation on Cloud Computing for IoT

Combinatorial auction is a popular approach for resource allocation in cloud computing. One of the challenges in resource allocation is that QoS (Quality of Service) constraints are satisfied and provider's profit is maximized. In order to increase the profit, the penalty cost for SLA (Service Level Agreement) violations needs to be reduced. We consider execution time constraint as SLA constraint in combinatorial auction system. In the system, we determine winners at each bidding round according to the job's urgency based on execution time deadline, in order to efficiently allocate resources and reduce the penalty cost. To analyze the performance of our mechanism, we compare the provider's profit and success rate of job completion with conventional mechanism using real workload data