Parallel and Distributed Computing.pptx
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The document discusses leveraging parallel and distributed computing in e-commerce data processing. It explores using these concepts to efficiently handle massive data volumes in e-commerce projects. Key topics covered include data partitioning, task parallelization, fault tolerance, scalability, and the benefits and disadvantages of these approaches. The conclusion states that parallel and distributed computing are powerful tools for e-commerce to extract insights, enhance customer experiences, and optimize operations in a data-centric environment.
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Parallel and Distributed Computing.pptx
- 1. PARALLEL AND DISTRIBUTED COMPUTING • DATE OF SUBMISSION: DECEMBER 2, 2023 • SUBMITTED BY: ABDULLAH JAMSHAID • SUBMITTED TO: DR. GULZAR AHMED • CLASS: BS CS 5TH AFTER ADP
- 2. LEVERAGING PARALLEL AND DISTRIBUTED COMPUTING IN E- COMMERCE DATA PROCESSING • E-COMMERCE THRIVES ON DATA, AND MANAGING VAST DATABASES IS CRUCIAL. THIS ASSIGNMENT EXPLORES USING PARALLEL AND DISTRIBUTED COMPUTING CONCEPTS TO EFFICIENTLY HANDLE MASSIVE DATA VOLUMES IN E-COMMERCE PROJECTS.
- 3. • DATA PARTITIONING • HORIZONTAL PARTITIONING (SHARDING): DIVIDING DATA LOGICALLY. • CONSIDERATION FOR DATA SKEWNESS: PREVENTING UNEVEN LOADS DUE TO DATA IMBALANCE. • TASK PARALLELIZATION • UTILIZING PARALLEL PROCESSING FRAMEWORKS: IMPLEMENTING SYSTEMS FOR PARALLEL EXECUTION. • OPTIMIZATION THROUGH PIPELINE PROCESSING: DESIGNING SEQUENTIAL PARALLEL ARCHITECTUARES. • FAULT TOLERANCE • REPLICATION AND REDUNDANCY: CREATING DATA COPIES ACROSS NODES. • CHECKPOINTING AND RECOVERY: SAVING INTERMEDIATE STATES FOR FAILURE RECOVERY. • SCALABILITY • CLOUD-BASED INFRASTRUCTURE: USING SERVICES FOR FLEXIBLE RESOURCE ALLOCATION. • AUTO-SCALING AND LOAD BALANCING: SETTING UP POLICIES FOR RESOURCE MANAGEMENT.
- 4. BENEFITS: • PERFORMANCE ENHANCEMENT: REDUCING COMPUTATION TIME BY HARNESSING MULTIPLE RESOURCES SIMULTANEOUSLY. • RESOURCE OPTIMIZATION: REDUCING IDLE TIME AND OPERATIONAL COSTS THROUGH EFFICIENT RESOURCE UTILIZATION. • ADAPTABILITY: FLEXIBILITY TO ACCOMMODATE GROWING DATA VOLUMES AND EVOLVING BUSINESS NEEDS. • EFFICIENCY AND COST-EFFECTIVENESS: OPTIMIZING RESOURCE USE AND LEVERAGING CLOUD-BASED INFRASTRUCTURE FOR COST SAVINGS. • DATA-DRIVEN DECISION MAKING: DEEPER INSIGHTS ENABLE STRATEGIC DECISIONS BASED ON CUSTOMER BEHAVIOR. • PERSONALIZED CUSTOMER EXPERIENCES: TAILORING RECOMMENDATIONS AND MARKETING BASED ON INDIVIDUAL PREFERENCES. • OPERATIONAL EFFICIENCY: PREDICTIVE ANALYTICS AIDING INVENTORY MANAGEMENT AND SUPPLY CHAIN OPTIMIZATION.
- 5. DISADVANTAGES: • INCREASED COMPLEXITY: DESIGNING AND MANAGING DISTRIBUTED SYSTEMS REQUIRE SPECIALIZED EXPERTISE. • COMMUNICATION OVERHEAD: FREQUENT NODE COMMUNICATION CAN CAUSE PERFORMANCE BOTTLENECKS. • SECURITY CONCERNS: DISTRIBUTED DATA INTRODUCES ADDITIONAL SECURITY CHALLENGES. • DEBUGGING DIFFICULTIES: TROUBLESHOOTING IN DISTRIBUTED SYSTEMS IS COMPLEX DUE TO DISTRIBUTED NATURE. • COST CONSIDERATIONS: INITIAL SETUP AND MANAGEMENT OF DISTRIBUTED SYSTEMS CAN BE COSTLY. • VENDOR LOCK-IN: DEPENDENCE ON SPECIFIC CLOUD PLATFORMS CAN LIMIT FUTURE CHOICES.
- 6. CONCLUSION: • PARALLEL AND DISTRIBUTED COMPUTING ARE POWERFUL TOOLS FOR E-COMMERCE. BY EFFICIENTLY HANDLING MASSIVE DATA VOLUMES, BUSINESSES CAN EXTRACT VALUABLE INSIGHTS, ENHANCE CUSTOMER EXPERIENCES, AND OPTIMIZE OPERATIONS. EMBRACING THESE TECHNOLOGIES IS CRUCIAL FOR E- COMMERCE TO THRIVE IN A DATA- CENTRIC ENVIRONMENT.