Dinamička replikacija datoteke zasnovana na mehanizmu opterećenosti i konzistencije CPU u pouzdan... more Dinamička replikacija datoteke zasnovana na mehanizmu opterećenosti i konzistencije CPU u pouzdanom distribuiranom okruženju Original scientific paper An effort has been made to propose a CPU load based dynamic, cooperative, trust based, and secure file replication approach based along with consistency among file replicas for distributed environment. Simulation results consisting of 100 requesting nodes, three file servers and file size ranging from 677 KB to 11 MB establishes that, when the CPU load is taken into consideration, the average decrease in file request completion time is about 22,04 ÷ 24,81 % thus optimizing the CPU load and minimizing the file request completion time. The CPU load decreases by 4,25 ÷ 5,58 %. Results show that, the average write latency with proposed mechanism decreases by 6,12 % as compared to Spinnaker writes and the average read latency is 3 times better than Cassandra Quorum Read (CQR). The proposed partial update propagation for maintaining file consistency stands to gain up to 69,67 % in terms of time required to update stale replicas. Thus the integrity of files and behaviour of the requesting nodes and file servers is guaranteed within even lesser time. Finally, a relationship between the formal aspects of simple security model and secure reliable CPU load based file replication model is established through process algebra. Keywords: CPU load balancing; distributed systems; file consistency; file encryption; file replication; role based credentials; trust management; update propagation and write invalidate Dinamička replikacija datoteke zasnovana na mehanizmu opterećenosti i konzistencije CPU u pouzdanom distribuiranom okruženju Izvorni znanstveni članak Pokušalo se predložiti dinamički, kooperativni, pouzdani i sigurni pristup replikaciji datoteke utemeljen na opterećenosti CPU uz konzistenciju među replikama datoteke za distribuirano okruženje. Rezultati simulacije koja se sastoji od 100 potrebnih čvorova, tri servera datoteke i datoteke veličine od 677 KB to 11 MB pokazuju da kada se uzme u obzir opterećenje CPU, prosječno smanjenje vremena potrebnog za popunjavanje datoteke je oko 22,04 ÷ 24,81 %. Tako se optimiziralo opterećenje CPU i smanjilo traženo vrijeme popunjavanja datoteke. Opterećenje CPU smanjuje se za 4,25 ÷ 5,58 %. Rezultati pokazuju da se prosječno kašnjenje upisa (write latency) s predloženim mehanizmom smanjuje za 6,12 % u usporedbi sa Spinnakerovim, a prosječno vrijeme čekanja čitanja (read latency) je 3 puta bolje od Cassandra Quorum Read (CQR). Predložena parcijalna propagacija ažuriranja za održavanje konzistencije datoteke povećava se do 69,67 % u odnosu na vrijeme potrebno za ažuriranje zastarjelih replika. Tako je integritet datoteka i ponašanje zahtijevanih čvorova i servera datoteke zagarantirano za čak manje vremena. Konačno, kroz algebra postupak uspostavljen je odnos između formalnih aspekata jednostavnog modela sigurnosti i sigurnog pouzdanog modela replikacije datoteke zasnovanog na sigurnom pouzdanom opterećenju datoteke. Ključne riječi: balansiranje CPU opterećenja; distribuirani sustavi; kodiranje datoteke; konzistencija datoteke; kvalifikacije utemeljene na ulozi; pouzdano upravljanje; propagacija ažuriranja i poništavanje zapisa; replikacija datoteke
Cloud computing offers an economical, convenient and elastic pool of computing resources over the... more Cloud computing offers an economical, convenient and elastic pool of computing resources over the internet. It enables computationally weak client to execute large computations by outsourcing their computation load to the cloud servers. However, outsourcing of data and computation to the third-party cloud servers bring multifarious security and privacy challenges that needed to be understood and address before the development of outsourcing algorithm. In this paper, the authors propose solutions for matrix-chain multiplication (MCM) problem. Our goal is to minimize the execution burden on the client without sacrificing the confidentiality and integrity of the input/output. Conventionally, the complexity of matrix-chain multiplication is O n 3. After leveraging the facility of outsourcing, the client-side complexity reduces to O n 2. In the proposed algorithm, the client employs some efficient linear transformation schemes, which preserve the data confidentiality. It also developed a novel result verification scheme, which verifies the result with modest burden and high probability and maintain the integrity of computed result. The analytical analysis of algorithm depicted that the algorithm is simultaneously meeting the design goals of correctness, security, efficiency and verifiability. We conduct many experiments to validate the algorithm and demonstrate its practical usability. The algorithm is implemented on public cloud " Amazon EC2 " , and found that the proposed outsource algorithm performs 11.655 times faster computation of matrix-chain multiplication than the direct implementation.
In the recent era, evolutionary meta-heuristic algorithms is popular research area in engineering... more In the recent era, evolutionary meta-heuristic algorithms is popular research area in engineering and scientific field. One of the intelligent evolutionary meta-heuristic algorithms is Teaching Learning Based Optimization (TLBO). The basic TLBO algorithm follows the isolated learning strategy for the whole population. This invariable learning strategy may cause the misconception of knowledge for a specific learner, which makes it unable to deal with different complex situations. For solving the complex non-linear optimization problems, local optimum frequently happens in the generating process. To resolve these kinds of problem, this paper introduces Neighbour based TLBO (NTLBO) and differential mutation. The concept of neighbour learning and differential mutation is introduced to improve the convergence solution after each run of experiment. Neighbour learning method maintains the explorative and exploitation search of the population and discourages the premature convergence. The efficiency of the proposed algorithm is evaluated on eight benchmark functions of Congress on Evolutionary Computation (CEC) 2006. The proposed NTLBO present extensive comparative study with the state-of-the-art forms of the meta-heuristic algorithms for standard benchmark functions. The result shows that the proposed NTLBO gives the superior performance over recent meta-heuristic algorithms
Cloud computing has become ubiquitous, offers an economical solution for convenient on-demand acc... more Cloud computing has become ubiquitous, offers an economical solution for convenient on-demand access to computing resources, which enable the resource-constrained clients to execute extensive computation. However, outsourcing of data and computation to the cloud server is a great cause of concern, such as confidentiality of input/output and verifiability of the result. This paper addresses the problem of designing outsourcing algorithm for linear regression analysis (LR), which is an important data analysis technique and widely applied across multiple domains. The outsourcing framework illustrated by the following scenario: a client is having a large dataset and needs to perform regression analysis, but unable to process due to lack of computing resources. Therefore, the client outsources the computation to the cloud server. In the proposed LR outsourcing algorithm, the client outsources LR problem to the cloud server without revealing to them either the input dataset and the output. The algorithm is a non-interactive solution to the client, it sends only input and receives output along with the proof of verification from the cloud server. The client in the proposed algorithm able to verify the correctness of result with an optimal probability. The analytical analysis shows that the algorithm is successfully meeting the challenges of correctness, security, verifiability, and efficiency. The experimental evaluation validates the proposed algorithm. The result analysis shows that the algorithm is highly efficient and endorses the practical usability of the algorithm.
Dinamička replikacija datoteke zasnovana na mehanizmu opterećenosti i konzistencije CPU u pouzdan... more Dinamička replikacija datoteke zasnovana na mehanizmu opterećenosti i konzistencije CPU u pouzdanom distribuiranom okruženju Original scientific paper An effort has been made to propose a CPU load based dynamic, cooperative, trust based, and secure file replication approach based along with consistency among file replicas for distributed environment. Simulation results consisting of 100 requesting nodes, three file servers and file size ranging from 677 KB to 11 MB establishes that, when the CPU load is taken into consideration, the average decrease in file request completion time is about 22,04 ÷ 24,81 % thus optimizing the CPU load and minimizing the file request completion time. The CPU load decreases by 4,25 ÷ 5,58 %. Results show that, the average write latency with proposed mechanism decreases by 6,12 % as compared to Spinnaker writes and the average read latency is 3 times better than Cassandra Quorum Read (CQR). The proposed partial update propagation for maintaining file consistency stands to gain up to 69,67 % in terms of time required to update stale replicas. Thus the integrity of files and behaviour of the requesting nodes and file servers is guaranteed within even lesser time. Finally, a relationship between the formal aspects of simple security model and secure reliable CPU load based file replication model is established through process algebra. Keywords: CPU load balancing; distributed systems; file consistency; file encryption; file replication; role based credentials; trust management; update propagation and write invalidate Dinamička replikacija datoteke zasnovana na mehanizmu opterećenosti i konzistencije CPU u pouzdanom distribuiranom okruženju Izvorni znanstveni članak Pokušalo se predložiti dinamički, kooperativni, pouzdani i sigurni pristup replikaciji datoteke utemeljen na opterećenosti CPU uz konzistenciju među replikama datoteke za distribuirano okruženje. Rezultati simulacije koja se sastoji od 100 potrebnih čvorova, tri servera datoteke i datoteke veličine od 677 KB to 11 MB pokazuju da kada se uzme u obzir opterećenje CPU, prosječno smanjenje vremena potrebnog za popunjavanje datoteke je oko 22,04 ÷ 24,81 %. Tako se optimiziralo opterećenje CPU i smanjilo traženo vrijeme popunjavanja datoteke. Opterećenje CPU smanjuje se za 4,25 ÷ 5,58 %. Rezultati pokazuju da se prosječno kašnjenje upisa (write latency) s predloženim mehanizmom smanjuje za 6,12 % u usporedbi sa Spinnakerovim, a prosječno vrijeme čekanja čitanja (read latency) je 3 puta bolje od Cassandra Quorum Read (CQR). Predložena parcijalna propagacija ažuriranja za održavanje konzistencije datoteke povećava se do 69,67 % u odnosu na vrijeme potrebno za ažuriranje zastarjelih replika. Tako je integritet datoteka i ponašanje zahtijevanih čvorova i servera datoteke zagarantirano za čak manje vremena. Konačno, kroz algebra postupak uspostavljen je odnos između formalnih aspekata jednostavnog modela sigurnosti i sigurnog pouzdanog modela replikacije datoteke zasnovanog na sigurnom pouzdanom opterećenju datoteke. Ključne riječi: balansiranje CPU opterećenja; distribuirani sustavi; kodiranje datoteke; konzistencija datoteke; kvalifikacije utemeljene na ulozi; pouzdano upravljanje; propagacija ažuriranja i poništavanje zapisa; replikacija datoteke
Cloud computing offers an economical, convenient and elastic pool of computing resources over the... more Cloud computing offers an economical, convenient and elastic pool of computing resources over the internet. It enables computationally weak client to execute large computations by outsourcing their computation load to the cloud servers. However, outsourcing of data and computation to the third-party cloud servers bring multifarious security and privacy challenges that needed to be understood and address before the development of outsourcing algorithm. In this paper, the authors propose solutions for matrix-chain multiplication (MCM) problem. Our goal is to minimize the execution burden on the client without sacrificing the confidentiality and integrity of the input/output. Conventionally, the complexity of matrix-chain multiplication is O n 3. After leveraging the facility of outsourcing, the client-side complexity reduces to O n 2. In the proposed algorithm, the client employs some efficient linear transformation schemes, which preserve the data confidentiality. It also developed a novel result verification scheme, which verifies the result with modest burden and high probability and maintain the integrity of computed result. The analytical analysis of algorithm depicted that the algorithm is simultaneously meeting the design goals of correctness, security, efficiency and verifiability. We conduct many experiments to validate the algorithm and demonstrate its practical usability. The algorithm is implemented on public cloud " Amazon EC2 " , and found that the proposed outsource algorithm performs 11.655 times faster computation of matrix-chain multiplication than the direct implementation.
In the recent era, evolutionary meta-heuristic algorithms is popular research area in engineering... more In the recent era, evolutionary meta-heuristic algorithms is popular research area in engineering and scientific field. One of the intelligent evolutionary meta-heuristic algorithms is Teaching Learning Based Optimization (TLBO). The basic TLBO algorithm follows the isolated learning strategy for the whole population. This invariable learning strategy may cause the misconception of knowledge for a specific learner, which makes it unable to deal with different complex situations. For solving the complex non-linear optimization problems, local optimum frequently happens in the generating process. To resolve these kinds of problem, this paper introduces Neighbour based TLBO (NTLBO) and differential mutation. The concept of neighbour learning and differential mutation is introduced to improve the convergence solution after each run of experiment. Neighbour learning method maintains the explorative and exploitation search of the population and discourages the premature convergence. The efficiency of the proposed algorithm is evaluated on eight benchmark functions of Congress on Evolutionary Computation (CEC) 2006. The proposed NTLBO present extensive comparative study with the state-of-the-art forms of the meta-heuristic algorithms for standard benchmark functions. The result shows that the proposed NTLBO gives the superior performance over recent meta-heuristic algorithms
Cloud computing has become ubiquitous, offers an economical solution for convenient on-demand acc... more Cloud computing has become ubiquitous, offers an economical solution for convenient on-demand access to computing resources, which enable the resource-constrained clients to execute extensive computation. However, outsourcing of data and computation to the cloud server is a great cause of concern, such as confidentiality of input/output and verifiability of the result. This paper addresses the problem of designing outsourcing algorithm for linear regression analysis (LR), which is an important data analysis technique and widely applied across multiple domains. The outsourcing framework illustrated by the following scenario: a client is having a large dataset and needs to perform regression analysis, but unable to process due to lack of computing resources. Therefore, the client outsources the computation to the cloud server. In the proposed LR outsourcing algorithm, the client outsources LR problem to the cloud server without revealing to them either the input dataset and the output. The algorithm is a non-interactive solution to the client, it sends only input and receives output along with the proof of verification from the cloud server. The client in the proposed algorithm able to verify the correctness of result with an optimal probability. The analytical analysis shows that the algorithm is successfully meeting the challenges of correctness, security, verifiability, and efficiency. The experimental evaluation validates the proposed algorithm. The result analysis shows that the algorithm is highly efficient and endorses the practical usability of the algorithm.
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Papers by Manu Vardhan